Healthcare, Aged Care & Community Services
A large Australian healthcare and community services organisation initiated a complex, multi-platform ERP transformation built on Microsoft Dynamics 365 within a highly regulated and deeply integrated environment.
Early assessment identified a critical issue:
Under a traditional testing model, the program would have required significant manual test design effort, large specialist teams, and sustained SME involvement to validate end-to-end finance, workforce, compliance, and operational processes. Testing effort would scale linearly with integration complexity increasing cost, delivery risk, and executive exposure.
The CIO required a different outcome:
Testpoint was engaged as the sole enterprise testing partner to fundamentally redesign how testing scaled across the program.
By deploying Vansah Intelligence – Testpoint’s Contextual AI platform, Testpoint replaced manual, document-driven test design with intelligence-driven generation and structured reuse.
Instead of scaling testing through people, testing scaled through contextual understanding of enterprise artefacts.
The result was a structural reduction in testing effort:
Testing shifted from a labor-intensive delivery risk to a governed, intelligence-led assurance capability.
The organisation was undertaking a large-scale transformation across multiple enterprise platforms in a highly regulated healthcare and public services environment. Success depended not just on individual system functionality, but on how complex, interdependent systems worked together across finance, workforce, and core operational processes.
This created three critical testing challenges:
The ERP platform supported multiple tightly integrated enterprise capability domains, including:
Each domain relied on complex integrations with workforce platforms, billing engines, identity services, government portals, and enterprise data platforms.
Validating these environments required deep understanding of:
A traditional testing approach would have required significant manual effort and sustained reliance on specialist resources to manage the scale and complexity of the program. This was not feasible.
Business SMEs were already embedded in delivery and operational roles, and scaling testing through additional headcount would have increased cost, coordination overhead, and delivery risk without improving assurance outcomes.
The limitations of a traditional testing model are summarised below:
| Traditional Testing Constraint | Impact on Delivery |
|---|---|
| Heavy reliance on specialised business SMEs | Ongoing dependency on scarce SME availability to interpret requirements, validate scenarios, and resolve ambiguity across complex, cross-functional processes |
| Large, specialist test teams | Requirement for sizeable teams with deep domain and system knowledge to manually design, review, and maintain test cases across integrated platforms |
| Manual, iterative test design cycles | Lengthy design, validation, and rework cycles due to repeated reviews, individual knowledge dependencies, and late identification of gaps or inconsistencies |
| Linear scaling of testing effort | Extended delivery timelines and increased execution risk as testing effort scaled with complexity and change rather than through reuse and intelligence |
The CIO required a solution that could scale and standardise test design and validation at enterprise level without compromising quality, assurance, or executive confidence. To meet these requirements, Testpoint defined a clear set of objectives and outcomes that guided the testing approach throughout the program.
| Objective | Desired Outcome |
|---|---|
| Manage enterprise-level complexity | Scale testing capability without increasing headcount |
| Reduce SME dependency | Retain business accuracy while reducing reliance on constant SME availability |
| Validate real operational scenarios | Deliver accurate, end-to-end test cases reflecting real business processes |
| Ensure UAT readiness | Enable business users to validate outcomes confidently during UAT |
| Use representative test data | Ensure test results were meaningful, compliant, and production-representative |
| Enable automation and regression | Support automation from design through execution for efficient regression |
| Maintain quality and governance | Preserve quality, traceability, and governance across a highly integrated environment |
Testpoint was engaged as the enterprise testing partner for the full 18-month program, accountable for transforming testing into a scalable, intelligence-driven capability that operated alongside the Tier-1 integrator.
Testpoint’s responsibilities included:
| Focus Area | Outcome & Value Delivered |
|---|---|
| Enterprise-wide test strategy | Defined a scalable test strategy aligned to complex, multi-release ERP delivery |
| Contextual AI and automation | Introduced Contextual AI and intelligent automation to improve testing accuracy, speed, and scale |
| Business-aligned test assets | Designed high-quality test assets reflecting true end-to-end business processes |
| Reduced SME dependency | Lowered reliance on scarce SMEs through structured, AI-assisted test design |
| Automation-ready by design | Ensured test assets were automation-ready, enabling efficient regression and reuse |
| Governance and readiness | Established strong governance, traceability, and execution readiness across SIT and UAT |
| Executive assurance | Provided leadership with clear, ongoing confidence in testing outcomes throughout the program |
Testpoint commenced with a structured discovery and assessment phase to fully understand the program’s complexity and delivery constraints.
This included:
Based on this discovery, Testpoint defined a fit-for-purpose enterprise test strategy, establishing:
| Capability | Purpose & Impact |
|---|---|
| Testing roadmap aligned to delivery and governance | Provided a clear, structured testing roadmap aligned to delivery milestones, governance requirements, and readiness checkpoints |
| Risk-based testing approach | Prioritised business-critical and highly integrated scenarios to focus effort where delivery and operational risk was highest |
| Early SIT and UAT readiness definition | Established clear SIT and UAT entry criteria early, reducing late-stage uncertainty and rework |
| Contextual AI ingestion model | Consolidated Level 1–4 requirements, business processes, solution designs, and technical integrations into a single governed structure |
| Scalable, reusable delivery model | Captured enterprise knowledge once and reused it across cycles, reducing duplication and dependency on individuals |
| Automation-ready testing foundation | Established a foundation for automation and repeatable delivery, ensuring long-term sustainability |
With the strategy established, Testpoint deployed Vansah Intelligence, its proprietary Contextual AI testing platform, as a core accelerator. Unlike generic AI or automation tools, Vansah understands enterprise context, including:
This shifted testing away from reliance on individual expertise and toward systematised, repeatable intelligence.
Vansah Intelligence was used to:
Traditional Testing vs Testpoint’s Contextual AI–Driven Approach
| Dimension | Traditional Testing Approach | Testpoint’s Approach |
|---|---|---|
| Primary Scaling Model | Scales through headcount and specialist resources | Scales through intelligence, reuse, and automation |
| SME Dependency | Sustained, ongoing involvement from highly specialised SMEs to interpret requirements and validate scenarios | SME knowledge captured once via Contextual AI ingestion and reused consistently, reducing ongoing dependency |
| Test Design Approach | Manual, document-driven test case creation based on fragmented artefacts | Context-aware test generation driven by AI ingesting requirements, design, and technical artefacts |
| Handling Complexity | Complexity increases effort linearly, requiring more people and longer cycles | Complexity managed through enterprise context, risk-based prioritisation, and structured reuse |
| Test Case Accuracy | Dependent on individual interpretation and manual reviews | Higher accuracy through normalised artefacts and contextual understanding of end-to-end processes |
| End-to-End Coverage | Often fragmented and difficult to maintain across integrated systems | End-to-end business scenarios designed and validated by default |
| Test Data Readiness | Test data often prepared late or inconsistently, impacting validity | Production-representative test data aligned to scenarios and automation requirements |
| UAT Readiness | Business users required to clarify scenarios and rework tests during UAT | UAT-ready test assets, enabling confident business validation with minimal rework |
| Automation Enablement | Automation treated as a downstream activity or afterthought | Automation-ready by design, enabled from test case generation through execution |
| Regression Capability | Regression suites costly to build and maintain manually | Reusable, scalable regression enabled through AI-driven design and automation frameworks |
| Governance & Traceability | Manual traceability with limited visibility and control | End-to-end traceability from requirements to execution |
| Delivery Impact | Longer design cycles, higher rework, increased delivery risk | Faster readiness, reduced rework, higher delivery confidence |
| Executive Confidence | Dependent on manual reporting and subjective assurance | Data-driven, auditable confidence in test readiness and outcomes |
To maximise long-term value, Testpoint conducted an Automation Proof of Concept (POC) to validate that Contextual AI-generated test assets could support scalable regression automation across the ERP program.
Focusing on high-risk, business-critical processes, the POC assessed test coverage, automation suitability, traceability, and reusability. Using Testpoint’s automation accelerators and enterprise testing toolchain, the team demonstrated how AI-generated test cases could transition seamlessly from test design to automated execution.
The POC confirmed that:
By validating automation feasibility early, Testpoint not only accelerated test readiness but also established a roadmap for sustainable quality engineering, reducing future testing effort while increasing release confidence and scalability.
Through a combination of enterprise test strategy, Contextual AI, intelligent automation, and proprietary accelerators, Testpoint transformed testing from a people-dependent, manual activity into an intelligence-driven, governed assurance capability accelerating readiness, reducing cost and delivery risk, and ensuring audit-ready compliance across a complex ERP transformation.
| Outcome | Impact |
|---|---|
| End-to-end business process assurance | Confidence that critical business processes would perform as designed across a highly integrated ERP environment, reducing operational disruption and compliance risk |
| 88% reduction in test readiness time | Test readiness compressed from six months to three weeks, enabling confident progression into SIT and UAT while maintaining full traceability and audit-ready evidence |
| Execution-ready test assets | Validated test assets reduced late rework and supported predictable, on-time testing cycles across multiple delivery streams |
| Reduced SME dependency | AI-driven test generation, validation, and reuse significantly reduced reliance on scarce SMEs and large specialist teams |
| Accelerated testing without quality trade-offs | Test design, validation, and regression effort compressed without sacrificing quality, keeping pace with delivery and integration complexity at enterprise scale |
| Scalable testing capability | Scalable Testing & Automation foundation, established a repeatable testing and test automation model, enabling efficient regression coverage and supporting future releases and business change without re-engineering test assets. |
| Governed, predictable assurance | Testing repositioned from a delivery risk to a governed assurance capability, providing leadership with clear visibility of readiness, risk, and release confidence |
Feedback captured from client executives and delivery partners following the review of Testpoint testing deliverables:
“As the testing deliverables were produced, it became clear that traditional testing would not scale for this program. Testpoint demonstrated how testing could scale through AI intelligence and structure, not by adding more people.”
“What would normally require months of SME effort was delivered as accurate, end-to-end test assets that were ready for execution and automation in two weeks.”
“From an executive perspective, seeing the quality and readiness of the test deliverables shifted testing from a delivery risk to a governed capability we could rely on.”
Testpoint helps enterprises deliver execution readiness through Contextual AI, intelligent automation, and enterprise testing leadership.
Speak to Testpoint about scaling testing with intelligence, not headcount.
Discover how a large Australian healthcare and community services organisation achieved enterprise-scale, audit-ready ERP test readiness in weeks not months.
